A novel clustering algorithm based Gaussian mixture model for image segmentation

Gaussian mixture model-based clustering algorithm is one of the advanced techniques applied to enhance the image segmentation performance. However, segmentation process is still encountering with some critical difficulties: the model is quite sensitive to initialization, and easily gets trapped in local maxima. To address these problems in image segmentation, we proposed a novel clustering algorithm by using the arbitrary covariance matrices for maximum likelihood estimation of GMMs. Such model can be able to prevent the effective use of population-base algorithms during clustering, and the arbitrary covariance matrices allow independently updating of individual parameters while retaining the validity of the matrix. The experimental results show that our method provides a simple segmentation process and the better quality of segmented images comparing to other methods. Furthermore, our method would provide an advanced technique for multi-dimensional image analysis and computer vision systems in varied sciences and technologies.